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The advent of new telecommunication systems with large bandwidths like 5G/6G and satellites operating at higher frequency bands such as Ku, Ka-band, and even Q/V bands, has brought to the forefront the issue of rain attenuation, particularly in equatorial and tropical regions. Many existing models developed to address these inaccuracies in the rainfall rate prediction rely on a single rain gauge measurement, which still can lead to inaccuracy when the model is generalized for a larger area. This research, therefore, is of utmost importance as it aims to develop an area-based rainfall rate model using multiple rain gauges spread across many locations in a specific area. The area of focus for this research is the Klang Valley area, a crucial economic territory that includes the capital city, Kuala Lumpur, in Malaysia. Five rain gauges distributed in Klang Valley were chosen to measure the rainfall rate. The rainfall rate model was then developed based on the data from these five rain gauges. The results indicate that each rain gauge's rainfall rate at R0.01% exceedance level varied greatly from 102 mm/hr to 138 mm/hr and exceeded Malaysia's recommended ITU-R at 100 mm/hr. The new model, presented herein, accounts for the variation of rainfall rate across a larger area, which can provide accurate modeling of specific attenuation and rain attenuation in the equatorial regions, thereby enhancing the reliability of communication systems. ABSTRAK: Kemunculan sistem telekomunikasi jalur lebar baru seperti 5G/6G dan satelit yang beroperasi pada jalur frekuensi tinggi seperti Ku, Ka-band, dan jalur Q/V, menyebabkan penurunan ketara amplitud gelombang akibat hujan, terutamanya di kawasan khatulistiwa dan tropika. Kebanyakan model sedia ada yang dibangunkan bagi menangani ketidak tepatan ramalan kadar hujan hanya berdasarkan pengukuran dari satu tolok hujan, ini menyebabkan ketidaktepatan model itu apabila digeneralisasikan bagi kawasan lebih luas. Oleh itu, kajian ini adalah amat penting kerana ia bertujuan bagi membangunkan model kadar hujan berasaskan kawasan menggunakan beberapa tolok hujan yang tersebar di pelbagai lokasi pada satu kawasan tertentu. Kawasan tumpuan kajian ini adalah kawasan Lembah Klang, sebuah wilayah ekonomi penting yang melingkupi ibu negara, Kuala Lumpur, Malaysia. Lima tolok hujan yang tersebar di Lembah Klang dipilih bagi mengukur kadar hujan. Model kadar hujan kemudiannya dibangunkan berdasarkan data dari kelima-lima tolok hujan ini. Dapatan menunjukkan kadar hujan R0.01% bagi lima tolok hujan adalah antara 102 mm/jam hingga 138 mm/jam dan melebihi kadar cadangan ITU-R Malaysia iaitu pada 100 mm/jam. Model baru yang mengambil kira variasi kadar hujan pada suatu kawasan lebih luas menggunakan banyak tolok hujan, di mana dapat memberikan pemodelan amplitud gelombang yang lebih tepat bagi hujan di kawasan khatulistiwa, dengan itu meningkatkan kebolehpercayaan sistem komunikasi.

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Characteristics of rainfall rate useful in the estimation of attenuation due to rain are presented. Rain data collected at Ogbomoso between January–October, 2009 were used in the analysis. Result shows that power law relationship exists between the equiprobable rain rates of two different integration times. The value of conversion factor CE and CR obtained for Ogbomoso are 0.28(60) and 0.64(90) respectively. Our result then shows that different conversion factor is required for different location even within the same climatic region.

A new comprehensive space-time model for the characterization of point rainfall rate is presented. A detailed assessment of four key rain characteristics (probability of rain/no rain condition, first and second order lognormal statistics and, space and time correlation functions) with consideration of the impact of varying spatial-temporal integration lengths are discussed. A set of empirical equations have been developed and the results show that they provide estimates of probability of rain/no rain with root mean square errors of less than 1.3 in space and 0.04 in time. They provide good estimates of the parameters at any space-time scales, particularly at higher resolutions that are of great importance to the design and planning of networks operating at frequencies above 10 GHz. In particular, the authors have created databases of rain characteristic parameters spanning North West Europe from which rain rate at any location of interest at different space-time scales can be conveniently obtained. These have been validated by comparing the rain rate exceedance distribution, $R _{0.01}$ , from the model estimates at different space-time scales across the British Isles with values calculated from measured data. It has been found that the proposed model gives highly accurate estimates of $R _{0.01}$ for the continental area with error percentages (E) generally less than 2.5 but the error percentage increases at the edges of the radar scans and in the oceanic area due to low data availability.

Predicting precipitation rate and rainfall attenuation are key aspects in planning and dimensioning of wireless communications systems operating at frequencies above 10 GHz, such as satellite communication systems at Ku and Ka bands. In this paper, contour maps of rainfall rate and rain attenuation are developed for the first time in Venezuela, based on 1-min rain rate statistics obtained from measurements carried out in Venezuela over at least 30 years period with a higher integration time and using Rice-Holmberg model, refined Moupfouma-Martin model and Recommendation ITU-R P.837-7, for rain rate estimation and Recommendation ITU-R P.618-13, Ramachandran-Kumar model, Yeo-Lee-Ong model and Rakshit-Adhikari-Maitra model, for rain attenuation prediction in “Simon Bolivar” satellite links in Venezuela. The overall results of both types of maps represent a useful tool for preliminary planning of those links in the country, specifically, in Ku and Ka bands

Increasing earth temperature due to the high level of greenhouse gas has affected the other climate elements on the earth surface. However, there was a little consideration of the differences between the global and local scale of climate change. This research included a comparison between the global climate change and the changes of climate elements that happened in Samawah city. The results showed that there is a significant difference between the increasing annual mean temperature globally and the annual mean temperature in the city. For example, when comparing the increase in annual mean temperature of the city for the period from 2006 to 2015 (ten years) with annual mean temperature globally based on the average of mean temperature for the previous 30 years (1975-2005), we note that there was increasing for the years 2010 and 2011 is almost four times and it was double for the years 2012, 2013, 2014 and 2015 higher than the increasing of annual mean temperature for the same years globally. Also, the mean temperature of the hot months of the summer season (JJA) in the city increased significantly compared to the rest of the months of the year and it is quite different from the change that occurred in the monthly mean temperature globally. Moreover, the annual average of precipitation in the city has fluctuated significantly due to the changes in climate and contrast to the increase in its global average. All these changes in the climate of the city will inevitably have an impact on the environment such as increasing drought, desertification and dust storms.

A realistic approach for gathering high-resolution observations of the rainfall rate, R, in the vertical plane is to use data from vertically pointing Doppler radars. After accounting for the vertical air velocity and attenuation, it is possible to determine the fine, spatially resolved drop size spectra and to calculate R for further statistical analyses. The first such results in a vertical plane are reported here. Specifically, we present results using MRR-Pro Doppler radar observations at resolutions of ten meters in height over the lowest 1.28 km, as well as ten seconds in time, over four sets of observations using two different radars at different locations. Both the correlation functions and power spectra are useful for translating observations and numerical model outputs of R from one scale down to other scales that may be more appropriate for particular applications, such as flood warnings and soil erosion, for example. However, it was found in all cases that, while locally applicable radial power spectra could be calculated, because of statistical heterogeneity most of the power spectra lost all generality, and proper correlation functions could not be computed in general except for one 17-min interval. Nevertheless, these results are still useful since they can be combined to develop catalogs of power spectra over different meteorological conditions and in different climatological settings and locations. Furthermore, even with the limitations of these data, this approach is being used to gain a deeper understanding of rainfall to be reported in a forthcoming paper.

Eight microwave links operating at frequencies ranging from 6 to 8 GHz and with path lengths ranging from 5.7 to 37.4 km traversing the city of Seoul, Korea are used to detect rainfall and estimate path-averaged rainfall rates. Rainfall detection using rain-induced attenuation (dB) was validated by rain detectors installed at automatic weather stations, and the results confirmed that microwave links can be used to detect rainfall with an accuracy ≥80%. The power-law R-k relationships between rain-induced specific attenuation, k (dB km−1), and the rainfall rate, R (mm h−1), were established and cross-validated by estimating the path-averaged rainfall rate. The mean bias of the path-averaged rainfall rate, as compared to the rainfall rate from ground rain gauges, was between −3 and 1 mm h−1. The improved accuracy of rainfall detection led to the improved accuracy of the path-averaged rainfall rate. Hence, it was confirmed that microwave links, used for broadcasting and media communications, can identify rainy or dry periods (rain spells or dry spells) in a way comparable to rain detectors and provide high time-resolution rainfall rates in real time.

Raindrop size distributions collected by the DROPLET MEASUREMENT TECHNOLogies Precipitation imaging probe from 17 flights through 6 hurricanes during National Oceanic and Atmospheric Administration's hurricane field program in 2020 are used to study reflectivity (Z) and rainfall rate (RR) (R) relationship (i.e., Z‐R relationship). The results show that the Z‐R distribution is highly scattered and the scatter increases with RR and reflectivity up to 48 dBZ or 25 mm hr−1, after which it decreases rapidly. The range of the estimated RR from a power‐law Z‐R relationship can be as large as 50 mm hr−1 at reflectivity of 40 dBZ. The result from random forest regression model demonstrates that including the information of mass‐weighted‐diameter (Dm) along with radar reflectivity improves the estimated RR significantly. Plain Language Summary: One of the main applications of radar measurements is to estimate rainfall rate (RR) based on its power‐law relationships with radar reflectivity. Different relationships have been obtained for different weather scenarios. Hurricanes, in comparison with other severe weather systems, have a much longer life cycle and travel across much greater zonal and meridional extent. Therefore, in order to accurately estimate the RR for a specific time at a specific location in a hurricane, the power‐law relationship with constant coefficients needs to be improved. The uncertainties of the estimated RR using power‐law relationship are related to the raindrop sizes. The usage of a machine learning model (random forest regression) demonstrates that the uncertainties in the estimated rainfall are reduced significantly when both radar reflectivity and the drop sizes are taken into account. Key Points: The scatter in reflectivity (Z) ‐rainfall rate (R) distribution increases up to 48 dBZ or 25 mm hr−1, after which it decreases rapidlyThere are significant uncertainties associated with using a power law Z‐R relationship to estimate rainfall rate from radar reflectivityThe result from a machine learning model shows that rainfall estimation is improved if drop size information and reflectivity are both used [ABSTRACT FROM AUTHOR]

This article addresses the opportunistic use of satellite downlink attenuation measurements for the site-specific estimation of rainfall rate. In particular, this article proposes a regression model characterized by the introduction of correction elements extracted from the frequency-domain analysis of the attenuation time series, and the use of a simple linear regression function with a small modification, which allows to using a negative intercept with the goal of minimizing the maximum error (large underestimation/overestimation). The performance, assessed using attenuation measurements of a satellite downlink in Q/V band shows that the proposed model allows to estimate the rainfall rate with low average error and low maximum error with respect to a more standard ITU model, so as to offer more reliable estimations. [ABSTRACT FROM AUTHOR]

Raindrop size distribution （RSD） is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation， especially in complex terrain with complicated rainfall mechanism and high spatial and temporal variability.In this paper， the RSD characteristics of different rainfall rates and rain types in the mountainous and plain areas of the Ili River Valley during the summer of 2020 -2022 are investigated using Parsivel2 disdrometer.The results show that the precipitation in the mountainous and plain areas is mainly composed of small raindrops， and medium raindrops are the largest contribution to the rainfall rate R.Precipitation in the mountainous and plain areas mainly occurs during the late afternoon and early evening， and the concentration of large and medium-sized raindrops is higher in the mountainous areas in all time periods.The spectral width and the concentration of drops with diameters increase with increasing rainfall rate.The number concentration of medium and large particles in the mountainous areas is obviously higher than that in the plain areas during light rain and heavy rain.For the same rainfall rate and rain type， precipitation in the mountainous areas has higher mass-weighted mean diameter （Dm） and lower normalized intercept parameter （log10Nw）.In addition， the convective rain spectrum in the mountainous and plain areas of the Ili River Valley tend to be “continental-like convective cluster”.It is found that there are also significant differences in the μ-Λ relationship and Z-R relationship of precipitation between the two areas， and the empirical relationship of Z =300R1.40 clearly overestimates precipitation.The results reveal the microphysical characteristics of precipitation under different terrain conditions in the Ili River Valley， and provide a reliable factual basis for the subsequent retrieval of precipitation using radar data.

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)'s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms. [ABSTRACT FROM AUTHOR]

The convective rainfall rate from cloud physical properties (CRPh) algorithm for Meteosat second-generation satellites is a day-only precipitation algorithm developed at the Spanish Meteorological Agency (AEMET) for EUMETSAT’ Satellite Application Facility in support of nowcasting and very short-range forecasting (NWC SAF). It is therefore mainly intended to provide input for monitoring and near-real-time forecasts for a few hours. This letter critically discusses the theoretical basis of the algorithm with special emphasis on the empirical values and assumptions in the microphysics of precipitation, and compares the qualitative performances of the CRPh with its antecessor, the convective rainfall rate algorithm (CRR), using an object-based method applied to a case-study. The analyses show that AEMET’s CRPh is physically consistent and outperforms the CRR. The applicability of the algorithm for nowcasting and the challenges of improving the product to an all-day algorithm are also presented.

The dual peak semidiurnal variation in surface rainfall rate over the tropics, simulated using a 3.5-km-mesh Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for 26 - 31 December 2006, is analyzed and compared with data from the 17 year winter precipitation climatology of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Precipitation Radar (PR), and the same 6 day data of Global Satellite Mapping of Precipitation, as well as infrared data from geostationary satellites. We focus on land areas including southern Africa and the Amazon. Over these land areas, the NICAM simulation captures the primary peak in the afternoon and the secondary peak in the early morning, at similar times to those captured using TRMM data. In the PR observation, the primary peak of rainfall is mainly due to convective rain, whereas the secondary peak is due to stratiform rain. In the NICAM simulation, if a simple method is used for the classification of convective/stratiform rain, convective rain is dominant all day long, and the rainfall rate is generally higher than in the PR observation. Nevertheless, an analysis of deep convection (DC) areas indicates consistency between the observation and NICAM; the primary peak of rainfall rate occurs at the mature stage of the number of DC areas, whereas the secondary peak occurs when the mean size of DC areas is almost at its highest point. However, in the NICAM simulation, the relative magnitudes of the two peaks are not represented well, and the contribution of the stratiform rain is underestimated. The present study indicates that a high-resolution global nonhydrostatic model like NICAM has the potential to overcome the limitations of coarse-resolution general circulation models by reproducing the semidiurnal variation of DC, although there is room for improvement. [ABSTRACT FROM AUTHOR]

This study investigates rainfall distribution by estimating rainfall rate from multi-source data of rain gauge, radar image and TRMM in Klang Valley, Malaysia. The study is also looking into rainfall intensity to identify rainfall types based on 35mm/hr thresholds for 5-minute interval using ground rain gauge data measurement. The results revealed that during the study period, the stratiform rainfall type was found dominant, and most of the rainfall events occurred during the evening. The simple regression and bias error analysis have conducted to assess the potential of radar image and TRMM for rainfall estimation rate. It has shown a positive but relatively weak relationship of regression coefficient between the rain gauge measurements and both data sources. The results indicated that radar image has better performance than TRMM satellite in rainfall rate estimation over Klang Valley. The radar has estimated a total rainfall rate of about 42.5mm/hour with percent bias are (-14.49%) of error relative to rain gauge data measurement. Meanwhile, the per cent bias for TRMM tends to underestimate the rainfall measurement by (-42.05%) with only 28.8mm/hour total rainfall rate were estimated. The spatial interpolation of the IDW technique reveals the rainfall distribution pattern in the study area, interpolated rainfall distribution from a radar image has shown a good agreement with rainfall distribution from rain gauge data measurement. Although radar image has higher accuracy in rainfall rate, estimation due to limited data availability used in this study was unable to reveal the rainfall pattern. Furthermore, both data products used in this study show a lower ability to detect high-intensity rainfall events due to limited data availability appropriately. [ABSTRACT FROM AUTHOR]